Graph-based prediction for contact suggestion in a location sharing system

ABSTRACT

Methods, systems, and devices for generating contact suggestions for a user of a social network. A first score is computed for each one of the plurality of users, the first score being computed using an edge-weighted ranking algorithm based on the user graph. A second score is computed, using a machine learning model, for each user of the plurality of users, the second score of each user being, at least partially, based on the first score of said user, with the second score of each user being representative of a probability of a first user sending a connection request to said user. A ranked contact suggestion list of one or more users of the plurality of users is generated, the one or more users being ranked based on their respective second score.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/353,604, filed Mar. 14, 2019, which is incorporated by referenceherein in its entirety.

BACKGROUND

The popularity of electronic messaging, particularly instant messaging,continues to grow. Users increasingly share media content items such aselectronic images and videos with each other, reflecting a global demandto communicate more visually. Similarly, users increasingly seek tocustomize the media content items they share with others, providingchallenges to social networking systems seeking to generate custom mediacontent for their members. Embodiments of the present disclosure addressthese and other issues.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure number in which that element is first introduced.

FIG. 1 is a diagrammatic representation of a networked environment inwhich the present disclosure may be deployed, in accordance with someexample embodiments.

FIG. 2 is a diagrammatic representation of a data structure asmaintained in a database, in accordance with some example embodiments.

FIG. 3 is a diagrammatic representation of a processing environment, inaccordance with some example embodiments.

FIG. 4 is block diagram showing a software architecture within which thepresent disclosure may be implemented, in accordance with some exampleembodiments.

FIG. 5 is a diagrammatic representation of a machine, in the form of acomputer system within which a set of instructions may be executed forcausing the machine to perform any one or more of the methodologiesdiscussed, in accordance with some example embodiments.

FIG. 6 illustrates a method in accordance with one embodiment.

FIG. 7 illustrates a method in accordance with one embodiment.

FIG. 8 illustrates a method in accordance with one embodiment.

FIG. 9 illustrates a method in accordance with one embodiment.

FIG. 10 illustrates a model in accordance with one embodiment.

FIG. 11 illustrates a user interface in accordance with one embodiment.

DETAILED DESCRIPTION

Various embodiments of the present disclosure provide systems, methods,techniques, instruction sequences, and computing machine programproducts for generating contact suggestions for a user of a socialnetwork.

Conventional methods for generating contact suggestions for a user of asocial network are limited in their ability to generate relevant contactsuggestions. Motivated by these challenges, some embodiments of thepresent disclosure provide improvements over conventional methods forgenerating contact suggestions by building a user graph and generatingcontact suggestions based on the user graph. In some embodiments, thecontact suggestions are then ranked using a machine learning modeltrained to predict a probability of a user sending a connection requestto another user based on being presented with the other user as acontact suggestion.

For example, in some embodiments, user data of a plurality of users areaccessed. A user graph of the plurality of users is generated. In theuser graph, two users that have a relationship (e.g., “friends” in thesocial network graph) are connected via a weighted edge. The weight ofthe weighted edge between the two users is based, at least partially, onan amount of time the two users that have a relationship (e.g.,“friends” in the social network graph) have spent together. A firstscore is computed for each one of the plurality of users, the firstscore being computed using an edge-weighted, edge-based rankingalgorithm based on the user graph. A second score is computed, using amachine learning model, for each user of the plurality of users, thesecond score of each user being, at least partially, based on the firstscore of said user, the second score of each user being representativeof a probability of a first user sending a connection request to saiduser. A ranked contact suggestion list of one or more users of theplurality of users is generated, with the one or more users being rankedbased on their respective second score. The ranked list is displayed ona client device of a first user.

The present disclosure provides various improvements over conventionaluser interfaces. In particular, in some embodiments, the amount of timeusers spend with the users they are connected to (e.g., “friends” in asocial network graph) is tracked, and the contact suggestion listdisplayed on the graphical user interface (GUI) is updated based on theamount of time the users have been spending together, automaticallymoving the contact suggestions with which the first user is most likelyto interact to a higher position on the GUI based on the determinedamount of time the users have been spending together.

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

FIG. 1 is a block diagram showing an example location sharing system 100for exchanging location data over a network. The location sharing system100 includes multiple instances of a client device 102, each of whichhosts a number of applications including a location sharing clientapplication 104. Each location sharing client application 104 iscommunicatively coupled to other instances of the location sharingclient application 104 and a location sharing server system 108 via anetwork 106 (e.g., the Internet).

A location sharing client application 104 is able to communicate andexchange data with another location sharing client application 104 andwith the location sharing server system 108 via the network 106. Thedata exchanged between location sharing client application 104, andbetween a location sharing client application 104 and the locationsharing server system 108, includes functions (e.g., commands to invokefunctions) and payload data (e.g., location data, text, audio, video orother multimedia data).

The location sharing server system 108 provides server-sidefunctionality via the network 106 to a particular location sharingclient application 104. While certain functions of the location sharingsystem 100 are described herein as being performed by either a locationsharing client application 104 or by the location sharing server system108, the location of certain functionality either within the locationsharing client application 104 or the location sharing server system 108is a design choice. For example, it may be technically preferable toinitially deploy certain technology and functionality within thelocation sharing server system 108 but to later migrate this technologyand functionality to the location sharing client application 104 where aclient device 102 has a sufficient processing capacity.

The location sharing server system 108 supports various services andoperations that are provided to the location sharing client application104. Such operations include transmitting data to, receiving data from,and processing data generated by the location sharing client application104. This data may include geolocation information, message content,client device information, media annotation and overlays, messagecontent persistence conditions, social network information, and liveevent information, as examples. Data exchanges within the locationsharing system 100 are invoked and controlled through functionsavailable via user interfaces (UIs) of the location sharing clientapplication 104.

Turning now specifically to the location sharing server system 108, anApplication Program Interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

The API server 110 receives and transmits message data (e.g., commandsand message payloads) between the client device 102 and the applicationserver 112. Specifically, the API server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the location sharing client application 104 in order to invokefunctionality of the application server 112. The API server 110 exposesvarious functions supported by the application server 112, includingaccount registration; login functionality; the sending of messages, viathe application server 112, from a particular location sharing clientapplication 104 to another location sharing client application 104; thesending of media files (e.g., images or video) from a location sharingclient application 104 to the location sharing server application 114and for possible access by another location sharing client application104; the setting of a collection of media data (e.g., story); theretrieval of a list of friends of a user of a client device 102; theretrieval of such collections; the retrieval of messages and content;the addition and deletion of friends to a social graph; the location offriends within a social graph; and opening an application event (e.g.,relating to the location sharing client application 104).

The application server 112 hosts a number of applications andsubsystems, including a location sharing server application 114, amessaging server application 116, and a social network system 122.

Examples of functions and services supported by the location sharingserver application 114 include generating a map GUI. In someembodiments, the map GUI may include representations of at leastapproximate respective positions of a user and a user's friends in asocial network graph accessed by the social media application usingavatars for each respective user.

The location sharing server application 114 may receive userauthorization to use, or refrain from using, the user's locationinformation. In some embodiments, the location sharing serverapplication 114 may likewise opt to share or not share the user'slocation with others via the map GUI. In some cases, the user's avatarmay be displayed to the user on the display screen of the user'scomputing device regardless of whether the user is sharing his or herlocation with other users.

In some embodiments, a user can select groups of other users (audiences)to which his/her location will be displayed and may specify differentdisplay attributes for the different respective groups or for differentrespective individuals. In one example, audience options include: “BestFriends,” “Friends,” and “Custom” (which is an individual-levelwhitelist of people). In this example, if “Friends” is selected, all newpeople added to the user's friends list will automatically be able tosee their location. If they are already sharing with the user, theiravatars will appear on the user's map.

In some embodiments, when viewing the map GUI, the user is able to seethe location of all his/her friends that have shared their location withthe user on the map, with each friend represented by their respectiveavatar. In some embodiments, if a friend does not have an avatar, thefriend may be represented using a profile picture or a default icondisplayed at the corresponding location for the friend.

In some embodiments, the user can select between friends on the map viaa menu, such as a carousel. In some embodiments, selecting a particularfriend automatically centers the map view on the avatar of that friend.Embodiments of the present disclosure may also allow the user to take avariety of actions with the user's friends from within the map GUI. Forexample, the system may allow the user to chat with the user's friendswithout leaving the map. In one particular example, the user may selecta chat icon from a menu presented in conjunction with the map GUI toinitiate a chat session.

The client device 102 host a messaging client application 124. Themessaging server application 116 implements a number of messageprocessing technologies and functions, particularly related to theaggregation and other processing of content (e.g., textual andmultimedia content) included in messages received from multipleinstances of the location sharing client application 104. As will bedescribed in further detail, the text and media content from multiplesources may be aggregated into collections of content (e.g., calledstories or galleries). These collections are then made available, by thelocation sharing server application 114, to the location sharing clientapplication 104. Other processor and memory intensive processing of datamay also be performed server-side by the location sharing serverapplication 114, in view of the hardware requirements for suchprocessing.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data processed by the location sharing server application 114.

The social network system 122 supports various social networkingfunctions and services, and makes these functions and services availableto the location sharing server application 114. To this end, the socialnetwork system 122 maintains and accesses an entity graph 30 a usergraph 204 (as shown in FIG. 2 ) within the database 120. Examples offunctions and services supported by the social network system 122include the identification of other users of the location sharing system100 with which a particular user has relationships or is “following” andalso the identification of other entities and interests of a particularuser.

FIG. 2 is a schematic diagram illustrating data structures 200 which maybe stored in the database 120 of the location sharing server system 108,according to certain example embodiments. While the content of thedatabase 120 is shown to comprise a number of tables, it will beappreciated that the data could be stored in other types of datastructures (e.g., as an object-oriented database).

The database 120 includes message data stored within a message table210. A user table 202 stores user data, including a user graph 204. Theuser graph 204 furthermore stores information regarding relationshipsand associations between users. Such relationships may be social,professional (e.g., work at a common corporation or organization)interested-based or activity-based, merely for example. Suchrelationships may include that a user is sharing his/her location withanother user via the location sharing system (e.g., location sharingsystem 100). Such relationships may include an amount of time (e.g.,tracked by the location sharing system 100) a user has spent withanother user. Such relationships may include that a user is a preferredcontact (e.g., “friend” in the social network graph) of another user ina social network (e.g., social network system 122). Such relationshipsmay include that a user is in an address book of another user in amessaging application (e.g., messaging client application 124). Suchrelationships may include that a user is in a contact book stored in aclient device (e.g., client device 102) of another user. The user table202 may also store the amount of time a user has spent with each of theusers with whom the given user has a relationship.

A location table 206 stores historical and current location data ofusers (e.g., geolocation information determined by a positioning system(e.g., GPS) unit of client devices (e.g., client device 102).

A training data table 208 stores training data comprising historicalcontact suggestions provided to users, and connection requests sent. Insome embodiments, the training data further comprises resultingconnections created based on users accepting a connection request. Theconnection request may be a request to connect via the location sharingsystem 100, and/or the messaging application 446, and/or the socialnetwork system 122.

Turning now to FIG. 3 , there is shown a diagrammatic representation ofa processing environment 300, which includes at least a processor 302(e.g., a GPU, CPU or combination thereof).

The processor 302 is shown to be coupled to a power source 304, and toinclude (either permanently configured or temporarily instantiated)modules, namely a location component 306, a link analysis component 308,a time component 310, a graph component 318, a machine learningcomponent 314, a training component 316, and a UI component 312. Thelocation component 306 determines a location of a user based on locationdata of the user collected by one or more client device (e.g., clientdevice 102) associated with the user. The time component 310 accesseslocation data from the location component 306, and tracks, for a givenuser, the amount of time the given user has spent with the users withwhom the given user has a relationship. In some embodiments, two usersare considered to spend time together when their respective location iswithin a preset distance of each other. The graph component 318 accessesuser data from the user table 202 and builds a user graph 204 based onthe user data. The link analysis component 308 performs a link analysisalgorithm on the graph to generate a ranked list of contact suggestions.The training component 316 accesses the training data and trains themachine learning component 314 on the task of predicting the probabilityof a user sending a connection request to another user. The machinelearning component 314 accesses the ranked list of contact suggestionsoutputted by the link analysis component and outputs a reranked contactsuggestion lists, ranked based on the probability of the user sending aconnection request to each user of the reranked contact suggestion list.The UI component 312 operationally generates user interfaces comprisingthe reranked list of contact suggestion and causes the user interfacesto be displayed on client devices.

FIG. 4 is a block diagram 400 illustrating a software architecture 404,which can be installed on any one or more of the devices describedherein. The software architecture 404 is supported by hardware such as amachine 402 that includes processors 420, memory 426, and I/O components438. In this example, the software architecture 404 can beconceptualized as a stack of layers, where each layer provides aparticular functionality. The software architecture 404 includes layerssuch as an operating system 412, libraries 410, frameworks 408, andapplications 406. Operationally, the applications 406 invoke API calls450 through the software stack and receive messages 452 in response tothe API calls 450.

The operating system 412 manages hardware resources and provides commonservices. The operating system 412 includes, for example, a kernel 414,services 416, and drivers 422. The kernel 414 acts as an abstractionlayer between the hardware and the other software layers. For example,the kernel 414 provides memory management, processor management (e.g.,scheduling), component management, networking, and security settings,among other functionality. The services 416 can provide other commonservices for the other software layers. The drivers 422 are responsiblefor controlling or interfacing with the underlying hardware. Forinstance, the drivers 422 can include display drivers, camera drivers,BLUETOOTH® or BLUETOOTH® Low Energy drivers, flash memory drivers,serial communication drivers (e.g., Universal Serial Bus (USB) drivers),WI-FI® drivers, audio drivers, power management drivers, and so forth.

The libraries 410 provide a low-level common infrastructure used by theapplications 406. The libraries 410 can include system libraries 418(e.g., C standard library) that provide functions such as memoryallocation functions, string manipulation functions, mathematicfunctions, and the like. In addition, the libraries 410 can include APIlibraries 424 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media formats such as MovingPicture Experts Group-4 (MPEG4), Advanced Video Coding (H.264 or AVC),Moving Picture Experts Group Layer-3 (MP3), Advanced Audio Coding (AAC),Adaptive Multi-Rate (AMR) audio codec, Joint Photographic Experts Group(JPEG or JPG), or Portable Network Graphics (PNG)), graphics libraries(e.g., an OpenGL framework used to render in two dimensions (2D) andthree dimensions (3D) in a graphic content on a display), databaselibraries (e.g., SQLite to provide various relational databasefunctions), web libraries (e.g., WebKit to provide web browsingfunctionality), and the like. The libraries 410 can also include a widevariety of other libraries 428 to provide many other APIs to theapplications 406.

The frameworks 408 provide a high-level common infrastructure that isused by the applications 406. For example, the frameworks 408 providevarious graphical user interface (GUI) functions, high-level resourcemanagement, and high-level location services. The frameworks 408 canprovide a broad spectrum of other APIs that can be used by theapplications 406, some of which may be specific to a particularoperating system or platform.

In an example embodiment, the applications 406 may include a homeapplication 436, a contacts application 430, a browser application 432,a book reader application 434, a location application 442, a mediaapplication 444, a messaging application 446, a game application 448,and a broad assortment of other applications such as third-partyapplications 440. The applications 406 are programs that executefunctions defined in the programs. Various programming languages can beemployed to create one or more of the applications 406, structured in avariety of manners, such as object-oriented programming languages (e.g.,Objective-C, Java, or C++) or procedural programming languages (e.g., Cor assembly language). In a specific example, the third-partyapplications 440 (e.g., applications developed using the ANDROID™ orIOS™ software development kit (SDK) by an entity other than the vendorof the particular platform) may be mobile software running on a mobileoperating system such as IOS™, ANDROID™, WINDOWS® Phone, or anothermobile operating system. In this example, the third-party applications440 can invoke the API calls 450 provided by the operating system 412 tofacilitate functionality described herein.

FIG. 5 is a diagrammatic representation of a machine 500 within whichinstructions 508 (e.g., software, a program, an application, an applet,an app, or other executable code) for causing the machine 500 to performany one or more of the methodologies discussed herein may be executed.For example, the instructions 508 may cause the machine 500 to executeany one or more of the methods described herein. The instructions 508transform the general, non-programmed machine 500 into a particularmachine 500 programmed to carry out the described and illustratedfunctions in the manner described. The machine 500 may operate as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 500 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 500 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), aPDA, an entertainment media system, a cellular telephone, a smart phone,a mobile device, a wearable device (e.g., a smart watch), a smart homedevice (e.g., a smart appliance), other smart devices, a web appliance,a network router, a network switch, a network bridge, or any machinecapable of executing the instructions 508, sequentially or otherwise,that specify actions to be taken by the machine 500. Further, while onlya single machine 500 is illustrated, the term “machine” shall also betaken to include a collection of machines that individually or jointlyexecute the instructions 508 to perform any one or more of themethodologies discussed herein.

The machine 500 may include processors 502, memory 504, and I/Ocomponents 542, which may be configured to communicate with each othervia a bus 544. In an example embodiment, the processors 502 (e.g., aCentral Processing Unit (CPU), a Reduced Instruction Set Computing(RISC) processor, a Complex Instruction Set Computing (CISC) processor,a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), anASIC, a Radio-Frequency Integrated Circuit (RFIC), another processor, orany suitable combination thereof) may include, for example, a processor506 and a processor 510 that execute the instructions 508. The term“processor” is intended to include multi-core processors that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.5 shows multiple processors 502, the machine 500 may include a singleprocessor with a single core, a single processor with multiple cores(e.g., a multi-core processor), multiple processors with a single core,multiple processors with multiples cores, or any combination thereof.

The memory 504 includes a main memory 512, a static memory 514, and astorage unit 516, both accessible to the processors 502 via the bus 544.The main memory 504, the static memory 514, and storage unit 516 storethe instructions 508 embodying any one or more of the methodologies orfunctions described herein. The instructions 508 may also reside,completely or partially, within the main memory 512, within the staticmemory 514, within machine-readable medium 518 within the storage unit516, within at least one of the processors 502 (e.g., within theprocessor's cache memory), or any suitable combination thereof, duringexecution thereof by the machine 500.

The I/O components 542 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 542 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones may include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 542 mayinclude many other components that are not shown in FIG. 5 . In variousexample embodiments, the I/O components 542 may include outputcomponents 528 and input components 530. The output components 528 mayinclude visual components (e.g., a display such as a plasma displaypanel (PDP), a light emitting diode (LED) display, a liquid crystaldisplay (LCD), a projector, or a cathode ray tube (CRT)), acousticcomponents (e.g., speakers), haptic components (e.g., a vibratory motor,resistance mechanisms), other signal generators, and so forth. The inputcomponents 530 may include alphanumeric input components (e.g., akeyboard, a touch screen configured to receive alphanumeric input, aphoto-optical keyboard, or other alphanumeric input components),point-based input components (e.g., a mouse, a touchpad, a trackball, ajoystick, a motion sensor, or another pointing instrument), tactileinput components (e.g., a physical button, a touch screen that provideslocation and/or force of touches or touch gestures, or other tactileinput components), audio input components (e.g., a microphone), and thelike.

In further example embodiments, the I/O components 542 may includebiometric components 532, motion components 534, environmentalcomponents 536, or position components 538, among a wide array of othercomponents. For example, the biometric components 532 include componentsto detect expressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram-based identification), and the like. The motioncomponents 534 include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope), and so forth. The environmental components536 include, for example, illumination sensor components (e.g.,photometer), temperature sensor components (e.g., one or morethermometers that detect ambient temperature), humidity sensorcomponents, pressure sensor components (e.g., barometer), acousticsensor components (e.g., one or more microphones that detect backgroundnoise), proximity sensor components (e.g., infrared sensors that detectnearby objects), gas sensors (e.g., gas detection sensors to detectionconcentrations of hazardous gases for safety or to measure pollutants inthe atmosphere), or other components that may provide indications,measurements, or signals corresponding to a surrounding physicalenvironment. The position components 538 include location sensorcomponents (e.g., a GPS receiver component), altitude sensor components(e.g., altimeters or barometers that detect air pressure from whichaltitude may be derived), orientation sensor components (e.g.,magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 542 further include communication components 540operable to couple the machine 500 to a network 520 or devices 522 via acoupling 524 and a coupling 526, respectively. For example, thecommunication components 540 may include a network interface componentor another suitable device to interface with the network 520. In furtherexamples, the communication components 540 may include wiredcommunication components, wireless communication components, cellularcommunication components, Near Field Communication (NFC) components,Bluetooth® components (e.g., Bluetooth® Low Energy), WiFi® components,and other communication components to provide communication via othermodalities. The devices 522 may be another machine or any of a widevariety of peripheral devices (e.g., a peripheral device coupled via aUSB).

Moreover, the communication components 540 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 540 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components540, such as location via Internet Protocol (IP) geolocation, locationvia Wi-Fi® signal triangulation, location via detecting an NFC beaconsignal that may indicate a particular location, and so forth.

The various memories (e.g., memory 504, main memory 512, static memory514, and/or memory of the processors 502) and/or storage unit 516 maystore one or more sets of instructions and data structures (e.g.,software) embodying or used by any one or more of the methodologies orfunctions described herein. These instructions (e.g., the instructions508), when executed by processors 502, cause various operations toimplement the disclosed embodiments.

The instructions 508 may be transmitted or received over the network520, using a transmission medium, via a network interface device (e.g.,a network interface component included in the communication components540) and using any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions508 may be transmitted or received using a transmission medium via thecoupling 526 (e.g., a peer-to-peer coupling) to the devices 522.

FIG. 6 is a flowchart illustrating a method 600 for generating a contactsuggestion list for a first user and displaying the contact suggestionlist to the first user. The method 600 may be embodied incomputer-readable instructions for execution by one or more processors(e.g., processor 302) such that the steps of the method 600 may beperformed in part or in whole by functional components (e.g., locationcomponent 308, link analysis component 308, a time component 312, agraph component 320, a machine learning component 316, trainingcomponent 318, and UI component 314) of a processing environment 300 ofa system (e.g., application server 112); accordingly, the method 600 isdescribed below by way of example with reference thereto. However, itshall be appreciated that the method 600 may be deployed on variousother hardware configurations and is not intended to be limited to thefunctional components of the processing environment 300.

In block 602, method 600 accesses, from a database (e.g., database 120)coupled to a server computer (e.g., application server 112), user dataof a plurality of users.

The system may need to receive authorization from the user to utilizeuser data prior to performing the remaining steps of method 600. Suchauthorization may be obtained via acceptance of a terms of service forutilizing an online social network or other service provided by thesystem, by acceptance on a case-by-case basis by the user (e.g., viapopups displayed on the user's computing device), or using any othersuitable method for obtaining authorization by the user(s).

The plurality of users is selected among all the users registered in auser table (e.g., user table 202) based on their relationship with thefirst user. The plurality of users may be users of a location trackingsystem, a social media application, and/or a messaging system. In someembodiments, the plurality of users are the users having at least eithera one-degree incoming relationship to the first user or a two-degreeoutgoing relationship from the first user.

A degree of a relationship between user u and user v is a number oflinks between user u and user v. If user u is in a direct relationshipwith user v, user u and user v are in a one-degree relationship. If useru is in a direct relationship with user w, and user w is in a directrelationship with user v, the user u and user v are in a two-degreerelationship.

A user u has an incoming relationship from user v when there is arelationship from user v to user u, such as, for example, user v issharing his/her location with user u via the location sharing system(e.g., location sharing system 100), user u is a preferred contact(e.g., “friend” in the social network graph) of user v in a socialnetwork (e.g., social network system 122), user u is in an address bookof user v in a messaging application (e.g., messaging application 446),or user u is in an address book stored in a client device (e.g., clientdevice 102) of user v.

A user u has an outgoing relationship to user v when there is arelationship from user u to user v, such as, for example, user u issharing his/her location with user v via the location sharing system(e.g., location sharing system 100), user u is a preferred contact(e.g., “friend” in the social network graph) of user v in a socialnetwork (e.g., social network system 122), user u is in an address bookof user v in a messaging application (e.g., messaging application 446),or user v is in an address book stored in a client device (e.g., clientdevice 102) of user u.

In block 604, method 600 generates, using one or more processors of theserver computer, a user graph (e.g., user graph 1002 of FIG. 10 ) of theplurality of users. The graph component 318 accesses the user table 202and builds the weighted graph based on the information retrieved fromthe user table 202. The graph includes an edge between every pair ofusers for which a relationship is recorded in the user table 202. Thegraph may be a directed graph.

The graph may be a weighted graph. The weight of the weighted edgebetween a user u and a user v may be based on one or more of thefollowing criteria:

-   -   for every pair of users (u, v), the weight of the weighted edge        from user u to user v is incremented if user v is included in a        group of users with which user u is sharing his/her position        (e.g., via the location sharing system 100);    -   for every pair of users (u, v), the weight of the weighted edge        from user u to user v is incremented if user v is included in a        group of user u, such as “Best Friends,” “Friends,” and “Custom”        (which is an individual-level whitelist of people) in a social        network (e.g., in the social network system 122);    -   for every pair of users (u, v), the weight of the weighted edge        from user u to user v is incremented if user v is a preferred        contact (e.g., “friend” in the social network graph) of user u        in a social network (e.g., social network system 122);    -   for every pair of users (u, v), the weight of the weighted edge        from user u to user v is incremented if user v is in an address        book of user u in a messaging application (e.g., messaging        application 446);    -   for every pair of users (u, v), the weight of the weighted edge        from user u to user v is incremented if user v is in an address        book stored in a client device of user u;    -   for every pair of users (u, v), the weight of the weighted edge        from user u to user v is based on whether user u sent a        connection request to user v. Such connection request may be an        invitation to share his/her position via the location sharing        system 100;    -   for every pair of users (u, v) that have a relationship (e.g.,        “friends” in the social network graph), a weight of a weighted        edge between user u and user v is based, at least partially, on        an amount of time user u and user v have spent together.

In block 606, method 600 (e.g., the link analysis component 308)computes, by the one or more processors, a first score for each one ofthe plurality of users, based on the graph. The first score is computedusing a link-based ranking algorithm (e.g., edge-weighted PageRank)based on the user graph (e.g., user graph 1002 of FIG. 10 ) of theplurality of users. In embodiments, if the graph is a weighted graph,the first score is computed using an edge-weighted edge-based rankingalgorithm (e.g., edge-weighted PageRank) based on the weighted usergraph.

The algorithm may be initialized to a same value for all users. At eachiteration, a contribution to the value PR (u) of a user u conferred byan edge from user v to user u is equal to the value PR(v) of user vdivided by the number of edges L(v) from user v weighted by the weightof the edge W(u, v) from user v to user u.

In embodiments, the value for any user can be expressed as:

${P{R(u)}} = {\sum\limits_{v \in B_{u}}{{W( {u,v} )}\frac{PR(v)}{L(v)}}}$

The value PR (u) for a user u is dependent on the values PR(v) for eachuser v contained in the set B_(u) (the set containing all users linkedto user u), divided by the number L(v) of links from the user v, andweighted by the weight W(u, v) of the link from user v to user u in caseof a directed graph.

In embodiments, the link analysis component 308 generates a ranked listof the users (e.g., ranked list 1004 of FIG. 10 ), with the users beingranked based on their respective first score.

In block 608, method 600 (e.g., the machine learning component 314)computes a second score for each user of the plurality of users. Thesecond score of a user may be computed as a probability of the firstuser sending a connection request to the user upon being presented witha suggestion to add the user as a contact. The second score of a usermay also be computed as a probability of the user accepting a connectionrequest sent by the first user. As explained in more details in relationto FIG. 7 , the second score of a user may be computed using a machinelearning model (e.g., machine learning model 1006 of FIG. 10 ).

The second score may depend on the age of the first user. For example,the score may be higher for a younger first user and lower for an olderfirst user. Indeed, it has been observed that younger users are morelikely to send a connection request to users with whom they have aweaker connection, while older users are more likely to only send aconnection request to users with whom they have a stronger connection.The score may also depend on an age difference between the first userand the user. The score may also depend on a distance between thelocation of the first user and the location of the user at computationtime. The score may also depend on the country of residency of the firstuser.

In block 610, method 600 (e.g., the machine learning component 316)generates a contact suggestion list (e.g., reranked list 1008 of FIG. 10) of one or more of the plurality of users, the contact suggestion listbeing ranked based on the second score of the plurality of users. Inembodiments, the users having a higher second score are ranked higher inthe contact suggestion list.

In block 612, method 600 (e.g., the UI component 314) causes display, ona client device of the first user, of the contact suggestion list (e.g.,user interface 1100 of FIG. 11 ).

As shown in FIG. 7 , the method 600 may further comprise a block 702 anda block 704, according to some embodiments. Consistent with someembodiments, block 702 may be performed before block 602, and block 704may be performed as part of (e.g., as sub-blocks or as a subroutine) ofblock 608, where the system computes a second score for each of theusers of the plurality of users.

In block 702, the method 600 (e.g., the training component 318) trains amachine learning model (e.g., machine learning model 1006) on a trainingset comprising historical user data. The machine learning model mayinclude an artificial neural network or a random forest classifier. Themachine learning model may include an artificial neural network, arandom forest classifier, or gradient boosted trees. Gradient-boostedtrees are based on an ensemble of tree-based classifiers that areiteratively optimized to minimize the prediction error.” In someembodiments, the historical user data comprises historical contactsuggestions and resulting connection requests (e.g., contact suggestionpresented to a user u to add a user v as a contact and a resultingconnection request sent from user u to user v). The connection requestmay be a request to connect via the location sharing system 100, and/orthe messaging application 446, and/or the social network system 122. Themachine learning model is trained on a learning task defined aspredicting a probability of a user sending a connection request toanother user.

In some embodiments, the historical data further comprises connectionscreated as a result of contact suggestions presented to users (e.g.,connection created from user u to user v, as a result of user vaccepting the connection request sent by user u). The connection may bea connection via the location sharing system 100, and/or the messagingapplication 446, and/or the social network system 122. The machinelearning model is trained on a learning task defined as predicting aprobability of a user accepting a connection request received formanother user.

In block 704, method 600 (e.g., the machine learning component 316)computes the second score using the trained machine learning model(e.g., machine learning model 1006). In some embodiments, the trainedmachine learning model computes the second score of each user as aprobability of the first user sending a connection request to said user.In some embodiments, the trained machine learning model computes thesecond score of each user as a probability of said user accepting aconnection request from the first user. In block 608, method 600 (e.g.,the machine learning component 314) computes the second score of eachuser, based, at least partially, on the second score of said user.

As shown in FIG. 8 , the method 600 may further comprise a block 802,according to some embodiments. Consistent with some embodiments, block802 may be performed as part of (e.g., as sub-blocks or as a subroutine)of block 610, where the system generates a contact suggestion listincluding one or more of the plurality of users.

In block 802, the method 600 filters out the users having a second scorebelow a threshold. The threshold is selected as a compromise between thenumber of contact suggestion and the relevance of the contactsuggestions.

In block 610, the contact suggestion list displayed on the displayscreen of the client device of the first user only includes users thathave a second score that exceeds the threshold.

As shown in FIG. 9 , the method 600 may further comprise a block 902, ablock 904, a block 906, a block 908, a block 910, a block 912, a block914, block 916 and a block 918 according to some embodiments. Consistentwith some embodiments, block 902, block 904, block 906, block 908, block910, block 912, block 914, and block 916 may be performed as part of(e.g., as sub-blocks or as a subroutine) of block 612, where the systemcauses display of the ranked contact suggestion list on the clientdevice of the first user.

In block 902, the method 600 receives new location data of one or moreusers of the plurality of users. In some embodiments, the locationsharing server application 114 receives, from one or more client devices(e.g., client device 102) associated with one or more of the pluralityof users, via a wireless communication, over a network (e.g., network106), an electronic communication containing current location data ofthe user. The current location data of the user may include locationdata gathered by one or more of the client devices of the user over arecent period of time. The system may receive location data on aperiodic basis or on an irregular basis and may request data from theclient device or receive such data from the client device without such arequest. In some embodiments, the client device contains software thatmonitors the location from the client device and transmits updates tothe system in response to detecting new location. For example, theclient device may update the system with a new location only after thelocation changes by at least a predetermined distance to allow a user tomove about a building or other location without triggering updates.

In block 904, method 600 (e.g., the time component 312) detects, basedon the new location data, that at least two users connected via an edgeof the user graph (e.g., users that are “friends” in the social networkgraph) have been spending time together. In embodiments, users areconsidered to spend time together when they are located within a maximumdistance of each other for a minimum amount of time. For example, thetime component 312 detects, based on location data received from user uand user v, that user u and user v have been spending time togethersince the last time the amount of time user u and user v had spenttogether was last computed.

In block 906, method 600 (e.g., the time component 312) computes, basedon detecting that user u and user v have been spending time togethersince the last time the amount of time user u and user v have spenttogether was computed, an updated amount of time user u and user v havespent together and updates the user table 202 with the updated amount oftime.

In block 908, method 600 (e.g., the graph component 320) computes, basedon the updated amount of time user u and user v have spent together, anupdated weight for the weighted edge between the user u and user v.

In block 910, method 600 (e.g., the graph component 320) generates,based on the updated weight, an updated user graph of the plurality ofusers.

In block 912, method 600 (e.g., the link analysis component 308)computes, based on the updated user graph, an updated first score foreach one of the plurality of users.

In block 914, method 600 (e.g., the machine learning component 316)computes, based on the updated first score of each of the plurality ofusers, an updated second score for each user of the plurality of users.

In block 916, method 600 (e.g., the contact suggestion component 316)generates an updated ranked contact suggestion list based on the updatedsecond score of each user of the plurality of users.

In block 918, method 600 (e.g., the UI component 314) automaticallycauses display, on the client device of the first user, of the updatedranked contact suggestion list. In embodiments, the UI component 314reorders the icons displayed on the user interface based on the updatedsecond score of each of the one or more users.

FIG. 10 illustrates a model 1000 implemented in some embodiments ofmethod 600. The model 1000 includes a user graph 1002 of the pluralityof users (e.g., implemented by the graph component 320) and a machinelearning model 1006 (e.g., implemented by the machine learning component316). The user graph 1002 is built based on user data related to a firstuser (user u-1). A link analysis algorithm is performed on the usergraph 1002 (e.g., by the link analysis component 308) and outputs aranked list 1004 of one or more users of the plurality of users. Theranked list 1004 is processed through the machine learning model 1006.The machine learning model 1006 outputs a reranked list 1008 comprisingthe one or more users of the ranked list 1004 ranked according to theirsecond score, with the users having a higher second score being rankedhigher in the reranked list 1008.

As shown in FIG. 11 , user interface 1100 is an example of a userinterface that may be displayed on a display screen of a client deviceof a first user. The user interface 1100 comprises a ranked contactsuggestion list comprising a plurality of contact suggestions 1102, witheach contact suggestion 1102 being associated with a user included inthe ranked contact suggestion list. Each contact suggestion 1102 isdisplayed alongside an interactive user interface element 1104, with theinteractive user interface element 1104 triggering the emission of aconnection request from the first user to the user associated with thecontact suggestion 1102.

Upon detecting a user interaction with one of the interactive userinterface elements 1104, a connection request is sent to the userassociated with the contact suggestion 1102. If the user associated withthe contact suggestion 1102 accepts the connection request, arelationship between the first user and the user is created. Therelationship is registered in the user table 202. In embodiments, theuser graph 204 is updated based on the updated user table 202, and theranked contact suggestion list displayed on the user interface 1100 isupdated based on the updated user graph 204, automatically moving thecontact suggestions with whom the first user is most likely to interactwith to a higher position on the user interface 1100 based on the newlycreated relationship.

In embodiments, the amount of time users that have a relationship (e.g.,“friends” in the social network graph) spend together is tracked, andthe ranked contact suggestion list displayed on the graphical userinterface 1100 is updated based on the amount of time the users thathave a relationship (e.g., are friends in the social network) have beenspending together, automatically moving the contact suggestions withwhom the first user is most likely to interact with to a higher positionon the user interface 1100 based on the determined amount of time theusers have been spending together.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific example embodiments, various modificationsand changes may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

“Signal Medium” refers to any intangible medium that is capable ofstoring, encoding, or carrying the instructions for execution by amachine and includes digital or analog communications signals or otherintangible media to facilitate communication of software or data. Theterm “signal medium” shall be taken to include any form of a modulateddata signal, carrier wave, and so forth. The term “modulated datasignal” means a signal that has one or more of its characteristics setor changed in such a matter as to encode information in the signal. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure.

“Communication Network” refers to one or more portions of a network thatmay be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, a network or a portion of a network may include awireless or cellular network and the coupling may be a Code DivisionMultiple Access (CDMA) connection, a Global System for Mobilecommunications (GSM) connection, or other types of cellular or wirelesscoupling. In this example, the coupling may implement any of a varietyof types of data transfer technology, such as Single Carrier RadioTransmission Technology (1×RTT), Evolution-Data Optimized (EVDO)technology, General Packet Radio Service (GPRS) technology, EnhancedData rates for GSM Evolution (EDGE) technology, third GenerationPartnership Project (3GPP) including 3G, fourth generation wireless (4G)networks, Universal Mobile Telecommunications System (UMTS), High SpeedPacket Access (HSPA), Worldwide Interoperability for Microwave Access(WiMAX), Long Term Evolution (LTE) standard, others defined by variousstandard-setting organizations, other long-range protocols, or otherdata transfer technology.

“Processor” refers to any circuit or virtual circuit (a physical circuitemulated by logic executing on an actual processor) that manipulatesdata values according to control signals (e.g., “commands”, “op codes”,“machine code”, etc.) and which produces corresponding output signalsthat are applied to operate a machine. A processor may, for example, bea Central Processing Unit (CPU), a Reduced Instruction Set Computing(RISC) processor, a Complex Instruction Set Computing (CISC) processor,a Graphics Processing Unit (GPU), a Digital Signal Processor (DSP), anApplication Specific Integrated Circuit (ASIC), a Radio-FrequencyIntegrated Circuit (RFIC) or any combination thereof. A processor mayfurther be a multi-core processor having two or more independentprocessors (sometimes referred to as “cores”) that may executeinstructions contemporaneously.

“Machine-Storage Medium” refers to a single or multiple storage devicesand/or media (e.g., a centralized or distributed database, and/orassociated caches and servers) that store executable instructions,routines and/or data. The term shall accordingly be taken to include,but not be limited to, solid-state memories, and optical and magneticmedia, including memory internal or external to processors. Specificexamples of machine-storage media, computer-storage media and/ordevice-storage media include non-volatile memory, including by way ofexample semiconductor memory devices, e.g., erasable programmableread-only memory (EPROM), electrically erasable programmable read-onlymemory (EEPROM), FPGA, and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks The terms “machine-storage medium,”“device-storage medium,” “computer-storage medium” mean the same thingand may be used interchangeably in this disclosure. The terms“machine-storage media,” “computer-storage media,” and “device-storagemedia” specifically exclude carrier waves, modulated data signals, andother such media, at least some of which are covered under the term“signal medium.”

“Component” refers to a device, physical entity, or logic havingboundaries defined by function or subroutine calls, branch points, APIs,or other technologies that provide for the partitioning ormodularization of particular processing or control functions. Componentsmay be combined via their interfaces with other components to carry outa machine process. A component may be a packaged functional hardwareunit designed for use with other components and a part of a program thatusually performs a particular function of related functions. Componentsmay constitute either software components (e.g., code embodied on amachine-readable medium) or hardware components. A “hardware component”is a tangible unit capable of performing certain operations and may beconfigured or arranged in a certain physical manner. In various exampleembodiments, one or more computer systems (e.g., a standalone computersystem, a client computer system, or a server computer system) or one ormore hardware components of a computer system (e.g., a processor or agroup of processors) may be configured by software (e.g., an applicationor application portion) as a hardware component that operates to performcertain operations as described herein. A hardware component may also beimplemented mechanically, electronically, or any suitable combinationthereof. For example, a hardware component may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware component may be a special-purpose processor,such as a field-programmable gate array (FPGA) or an applicationspecific integrated circuit (ASIC). A hardware component may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software), may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering embodiments in which hardwarecomponents are temporarily configured (e.g., programmed), each of thehardware components need not be configured or instantiated at any oneinstance in time. For example, where a hardware component comprises ageneral-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware component at one instanceof time and to constitute a different hardware component at a differentinstance of time. Hardware components can provide information to, andreceive information from, other hardware components. Accordingly, thedescribed hardware components may be regarded as being communicativelycoupled. Where multiple hardware components exist contemporaneously,communications may be achieved through signal transmission (e.g., overappropriate circuits and buses) between or among two or more of thehardware components. In embodiments in which multiple hardwarecomponents are configured or instantiated at different times,communications between such hardware components may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware components have access. Forexample, one hardware component may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware component may then, at alater time, access the memory device to retrieve and process the storedoutput. Hardware components may also initiate communications with inputor output devices, and can operate on a resource (e.g., a collection ofinformation). The various operations of example methods described hereinmay be performed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors 1004 orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an API). The performance ofcertain of the operations may be distributed among the processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processors orprocessor-implemented components may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented components may be distributed across a number ofgeographic locations.

“Carrier Signal” refers to any intangible medium that is capable ofstoring, encoding, or carrying instructions for execution by themachine, and includes digital or analog communications signals or otherintangible media to facilitate communication of such instructions.Instructions may be transmitted or received over a network using atransmission medium via a network interface device.

“Computer-Readable Medium” refers to both machine-storage media andtransmission media. Thus, the terms include both storage devices/mediaand carrier waves/modulated data signals. The terms “machine-readablemedium,” “computer-readable medium” and “device-readable medium” meanthe same thing and may be used interchangeably in this disclosure.

“Client Device” refers to any machine that interfaces to acommunications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smartphones, tablets, ultrabooks, netbooks, laptops,multi-processor systems, microprocessor-based or programmable consumerelectronics, game consoles, set-top boxes, or any other communicationdevice that a user may use to access a network.

“Ephemeral Message” refers to a message that is accessible for atime-limited duration. An ephemeral message may be a text, an image, avideo and the like. The access time for the ephemeral message may be setby the message sender. Alternatively, the access time may be a defaultsetting or a setting specified by the recipient. Regardless of thesetting technique, the message is transitory.

What is claimed is:
 1. A system comprising: one or more processors; anda memory storing instructions that, when executed by the one or moreprocessors, configure the system to perform operations comprising:accessing, from a database coupled to a server computer, user data of aplurality of users; generating, using one or more processors of theserver computer, a directed graph of the plurality of users, wherein aweight of a weighted edge between two users of the plurality of users isbased, at least partially, on an amount of time the two users have spenttogether; computing, by the one or more processors, a first score foreach user of the plurality of users, the first score of each user beingcomputed using an edge-weighted edge-based ranking algorithm based onthe directed graph, computing, by the one or more processors, using amachine learning model, a second score for each user of the plurality ofusers, the second score of each user being, at least partially, based onthe first score of the user, the second score for each user beingcomputed based on a first probability of a first user of the pluralityof users sending a connection request to the user, and on a secondprobability of the user accepting the connection request received fromthe first user; generating, by the one or more processors, a rankedcontact suggestion list of one or more users of the plurality of users,the one or more users being ranked based on their respective secondscore, and the one more users for presenting as contact suggestions tothe first user; and causing display, on a client device of the firstuser, of the ranked contact suggestion list.
 2. The system of claim 1,the operations further comprising: training the machine learning modelon a training set comprising a plurality of training examples, thetraining examples comprising historical contact suggestions and whetherthe historical contact suggestions resulted in connection requests, andon a learning task defined as predicting a probability of an exemplaryuser sending a connection request to another exemplary user, whereincomputing the second score for each user of the plurality of userscomprises computing, using the trained machine learning model, the firstprobability of the first user sending the connection request to theuser.
 3. The system of claim 2, wherein the training examples furthercomprise whether the historical contact suggestions resulted inconnections, wherein the learning task is further defined as predictinga probability the exemplary user accepting an invitation requestreceived from the other exemplary user, and wherein computing the secondscore for each user of the plurality of users further comprisescomputing, using the trained machine learning model, the secondprobability of the user accepting the connection request received fromthe first user.
 4. The system of claim 1, wherein the one or more usersof the plurality of users are the users having a second score thatexceeds a threshold.
 5. The system of claim 1, wherein causing displayof the ranked contact suggestion list comprises causing display of auser interface comprising a plurality of icons, each icon beingassociated with one user of the one or more users of the ranked contactsuggestion list, a location of each icon on the user interface beingbased on the second score of the user associated with the icon.
 6. Thesystem of claim 5, the operations further comprising: generating, inresponse to detecting a user interaction with one icon of the pluralityof icons, a connection request from the first user to the userassociated with the one icon.
 7. The system of claim 1, the operationsfurther comprising: accessing an address book stored in a client deviceof each user of the plurality of users, wherein the weight of theweighted edge from the first user to a second user of the plurality ofusers is based on whether the second user is included in the addressbook of the first user.
 8. The system of claim 1, wherein the weight ofthe weighted edge from the first user to a second user of the pluralityof users is based on whether the first user sent a connection request tothe second user.
 9. The system of claim 1, the operations furthercomprising: tracking a location of at least one user of the plurality ofusers; and dynamically updating the ranked contact suggestion list asnew location data of the at least one user is received.
 10. The systemof claim 9, wherein dynamically updating the ranked contact suggestionlist comprises: detecting that the first user and a second user of theplurality of users have been spending more time together since theamount of time the first and second users have spent together wascomputed; computing an updated amount of time the first and second usershave spent together; computing, based on the updated amount of time thefirst and second users have spent together, an updated weight for theweighted edge between the first and second users; generating, based onthe updated weight, an updated directed graph of the plurality of users;and computing, based on the updated directed graph, an updated firstscore for at least one user of the plurality of users; computing, basedon the updated first score of the at least one user, a second score forthe at least one user; and generating, based on the updated second scoreof the at least one user, an updated ranked list.
 11. A methodcomprising: accessing, from a database coupled to a server computer,user data of a plurality of users; generating, using one or moreprocessors of the server computer, a directed graph of the plurality ofusers, wherein a weight of a weighted edge between two users of theplurality of users is based, at least partially, on an amount of timethe two users have spent together; computing, by the one or moreprocessors, a first score for each user of the plurality of users, thefirst score of each user being computed using an edge-weightededge-based ranking algorithm based on the directed graph, computing, bythe one or more processors, using a machine learning model, a secondscore for each user of the plurality of users, the second score of eachuser being, at least partially, based on the first score of the user,the second score for each user being computed based on a firstprobability of a first user of the plurality of users sending aconnection request to the user, and on a second probability of the useraccepting the connection request received from the first user;generating, by the one or more processors, a ranked contact suggestionlist of one or more users of the plurality of users, the one or moreusers being ranked based on their respective second score, and the onemore users for presenting as contact suggestions to the first user; andcausing display, on a client device of the first user, of the rankedcontact suggestion list.
 12. The method of claim 11, further comprising:training the machine learning model on a training set comprising aplurality of training examples, the training examples comprisinghistorical contact suggestions and whether the historical contactsuggestions resulted in connection requests, and on a learning taskdefined as predicting a probability of an exemplary user sending aconnection request to another exemplary user, wherein computing thesecond score for each user of the plurality of users comprisescomputing, using the trained machine learning model, the firstprobability of the first user sending the connection request to theuser.
 13. The method of claim 12, wherein the training examples furthercomprise whether the historical contact suggestions resulted inconnections, wherein the learning task is further defined as predictinga probability the exemplary user accepting an invitation requestreceived from the other exemplary user, and wherein computing the secondscore for each user of the plurality of users further comprisescomputing, using the trained machine learning model, the secondprobability of the user accepting the connection request received fromthe first user.
 14. The method of claim 11, wherein the one or moreusers of the plurality of users are the users having a second score thatexceeds a threshold.
 15. The method of claim 11, wherein causing displayof the ranked contact suggestion list comprises causing display of auser interface comprising a plurality of icons, each icon beingassociated with one user of the one or more users of the ranked contactsuggestion list, a location of each icon on the user interface beingbased on the second score of the user associated with the icon.
 16. Themethod of claim 15, further comprising: generating, in response todetecting a user interaction with one icon of the plurality of icons, aconnection request from the first user to the user associated with theone icon.
 17. The method of claim 11, further comprising: accessing anaddress book stored in a client device of each user of the plurality ofusers, wherein the weight of the weighted edge from the first user to asecond user of the plurality of users is based on whether the seconduser is included in the address book of the first user.
 18. The methodof claim 11, wherein the weight of the weighted edge from the first userto a second user of the plurality of users is based on whether the firstuser sent a connection request to the second user.
 19. The method ofclaim 11, further comprising: tracking a location of at least one userof the plurality of users; and dynamically updating the ranked contactsuggestion list as new location data of the at least one user isreceived.
 20. A non-transitory computer-readable storage medium, thecomputer-readable storage medium including instructions that whenexecuted by a computer, cause the computer to perform operationscomprising: accessing, from a database coupled to a server computer,user data of a plurality of users; generating, using one or moreprocessors of the server computer, a directed graph of the plurality ofusers, wherein a weight of a weighted edge between two users of theplurality of users is based, at least partially, on an amount of timethe two users have spent together; computing, by the one or moreprocessors, a first score for each user of the plurality of users, thefirst score of each user being computed using an edge-weightededge-based ranking algorithm based on the directed graph, computing, bythe one or more processors, using a machine learning model, a secondscore for each user of the plurality of users, the second score of eachuser being, at least partially, based on the first score of the user,the second score for each user being computed based on a firstprobability of a first user of the plurality of users sending aconnection request to the user, and on a second probability of the useraccepting the connection request received from the first user;generating, by the one or more processors, a ranked contact suggestionlist of one or more users of the plurality of users, the one or moreusers being ranked based on their respective second score, and the onemore users for presenting as contact suggestions to the first user; andcausing display, on a client device of the first user, of the rankedcontact suggestion list.